
"""simple docstring"""


import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch

import numpy as np
import pytest
from absl.testing import parameterized

import datasets
from datasets import load_metric

from .utils import for_all_test_methods, local, slow


# mark all tests as integration
_lowercase				 =					pytest.mark.integration


_lowercase				 =					{"""comet"""}
_lowercase				 =					importlib.util.find_spec('''fairseq''') is not None

_lowercase				 =					{"""code_eval"""}
_lowercase				 =					os.name == """nt"""

_lowercase				 =					{"""bertscore""", """frugalscore""", """perplexity"""}
_lowercase				 =					importlib.util.find_spec('''transformers''') is not None






def    _snake_case					(		snake_case__ :  Any  ):



	@wraps(a__  )
	def wrapper(self :  int						,   snake_case__ :  str  ):
		if not _has_fairseq and metric_name in REQUIRE_FAIRSEQ:
			self.skipTest('\"test requires Fairseq\"'  )
		else:
			test_case(self						,   a__  )

	return wrapper






def    _snake_case					(		snake_case__ :  Dict  ):



	@wraps(a__  )
	def wrapper(self :  str						,   snake_case__ :  Union[str, Any]  ):
		if not _has_transformers and metric_name in REQUIRE_TRANSFORMERS:
			self.skipTest('\"test requires transformers\"'  )
		else:
			test_case(self						,   a__  )

	return wrapper






def    _snake_case					(		snake_case__ :  Any  ):



	@wraps(a__  )
	def wrapper(self :  List[Any]						,   snake_case__ :  Any  ):
		if _on_windows and metric_name in UNSUPPORTED_ON_WINDOWS:
			self.skipTest('\"test not supported on Windows\"'  )
		else:
			test_case(self						,   a__  )

	return wrapper






def    _snake_case					(		):
	A     = [metric_dir.split(os.sep  )[-2] for metric_dir in glob.glob('./metrics/*/'  )]
	return [{"testcase_name": x, "metric_name": x} for x in metrics if x != "gleu"]  # gleu is unfinished







@parameterized.named_parameters(get_local_metric_names()     )
@for_all_test_methods(
    a_		,       a_		,       a_     )
@local
class 					lowerCAmelCase_					(   parameterized.TestCase     ):



						'''simple docstring'''


						_lowerCamelCase:     Tuple   =     {}
						_lowerCamelCase:     Tuple   =     None


						@pytest.mark.filterwarnings('ignore:metric_module_factory is deprecated:FutureWarning'   )
						@pytest.mark.filterwarnings('ignore:load_metric is deprecated:FutureWarning'   )
						def     _SCREAMING_SNAKE_CASE   (			self  :   int	,A_  :   str   )  -> Tuple:
							A     = """[...]"""
							A     = importlib.import_module(
							    datasets.load.metric_module_factory(os.path.join('metrics'	,_lowercase   )   ).module_path   )
							A     = datasets.load.import_main_class(metric_module.__name__	,dataset=_lowercase   )
							# check parameters
							A     = inspect.signature(metric._compute   ).parameters
							self.assertTrue(all(p.kind != p.VAR_KEYWORD for p in parameters.values()   )   )  # no **kwargs
							# run doctest
							with self.patch_intensive_calls(_lowercase	,metric_module.__name__   ):
								with self.use_local_metrics():
									try:
										A     = doctest.testmod(_lowercase	,verbose=_lowercase	,raise_on_error=_lowercase   )
									except doctest.UnexpectedException as e:
										raise e.exc_info[1]  # raise the exception that doctest caught
							self.assertEqual(results.failed	,0   )
							self.assertGreater(results.attempted	,1   )


						@slow
						def     _SCREAMING_SNAKE_CASE   (			self  :   Union[str, Any]	,A_  :   List[Any]   )  -> Dict:
							A     = """[...]"""
							A     = importlib.import_module(
							    datasets.load.metric_module_factory(os.path.join('metrics'	,_lowercase   )   ).module_path   )
							# run doctest
							with self.use_local_metrics():
								A     = doctest.testmod(_lowercase	,verbose=_lowercase	,raise_on_error=_lowercase   )
							self.assertEqual(results.failed	,0   )
							self.assertGreater(results.attempted	,1   )


						@contextmanager
						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]	,A_  :   Optional[Any]	,A_  :   Any   )  -> Tuple:
							if metric_name in self.INTENSIVE_CALLS_PATCHER:
								with self.INTENSIVE_CALLS_PATCHER[metric_name](_lowercase   ):
									yield
							else:
								yield


						@contextmanager
						def     _SCREAMING_SNAKE_CASE   (			self  :   str   )  -> Any:
							def load_local_metric(A_  :   List[str]	,*A_  :   Optional[Any]	,**A_  :   Any   ):
								return load_metric(os.path.join('metrics'	,_lowercase   )	,*_lowercase	,**_lowercase   )

							with patch('datasets.load_metric'   ) as mock_load_metric:
								A     = load_local_metric
								yield




						@classmethod
						def     _SCREAMING_SNAKE_CASE   (			cls  :   List[Any]	,A_  :   List[Any]   )  -> int:
							def wrapper(A_  :   Optional[Any]   ):
								A     = contextmanager(_lowercase   )
								A     = patcher
								return patcher

							return wrapper






@LocalMetricTest.register_intensive_calls_patcher('bleurt'  )
def    _snake_case					(		snake_case__ :  Union[str, Any]  ):
	import tensorflow.compat.va as tf
	from bleurt.score import Predictor

	tf.flags.DEFINE_string('sv'						,   ''						,   ''  )  # handle pytest cli flags







	class 					lowerCAmelCase_					(   a_     ):



							'''simple docstring'''


							def     _SCREAMING_SNAKE_CASE   (			self  :   List[str]	,A_  :   Tuple   )  -> Union[str, Any]:
								assert len(input_dict['input_ids']   ) == 2
								return np.array([1.03, 1.04]   )

    # mock predict_fn which is supposed to do a forward pass with a bleurt model
	with patch('bleurt.score._create_predictor'  ) as mock_create_predictor:
		A     = MockedPredictor()
		yield






@LocalMetricTest.register_intensive_calls_patcher('bertscore'  )
def    _snake_case					(		snake_case__ :  Union[str, Any]  ):
	import torch

	def bert_cos_score_idf(snake_case__ :  Union[str, Any]						,   snake_case__ :  Optional[Any]						,   *snake_case__ :  List[str]						,   **snake_case__ :  List[str]  ):
		return torch.tensor([[1.0, 1.0, 1.0]] * len(a__  )  )

	# mock get_model which is supposed to do download a bert model
	# mock bert_cos_score_idf which is supposed to do a forward pass with a bert model
	with patch('bert_score.scorer.get_model'  ), patch(
	    'bert_score.scorer.bert_cos_score_idf'  ) as mock_bert_cos_score_idf:
		A     = bert_cos_score_idf
		yield






@LocalMetricTest.register_intensive_calls_patcher('comet'  )
def    _snake_case					(		snake_case__ :  Any  ):



	def load_from_checkpoint(snake_case__ :  Any  ):







		class 					lowerCAmelCase_					:



								'''simple docstring'''


								def     _SCREAMING_SNAKE_CASE   (			self  :   List[Any]	,A_  :   Union[str, Any]	,*A_  :   Any	,**A_  :   Any   )  -> int:
									assert len(_lowercase   ) == 2
									A     = [0.19, 0.92]
									return scores, sum(_lowercase   ) / len(_lowercase   )

		return Model()

	# mock load_from_checkpoint which is supposed to do download a bert model
	# mock load_from_checkpoint which is supposed to do download a bert model
	with patch('comet.download_model'  ) as mock_download_model:
		A     = None
		with patch('comet.load_from_checkpoint'  ) as mock_load_from_checkpoint:
			A     = load_from_checkpoint
			yield






def    _snake_case					(		):
	A     = load_metric(os.path.join('metrics'						,   'seqeval'  )  )
	A     = """ERROR"""
	A     = F'Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {wrong_scheme}'
	with pytest.raises(a__						,   match=re.escape(a__  )  ):
		metric.compute(predictions=[]						,   references=[]						,   scheme=a__  )


import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union

# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feature_extraction_utils import FeatureExtractionMixin
from ...utils import CONFIG_NAME, FEATURE_EXTRACTOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)


__snake_case				:      Union[str, Any]   		=			logging.get_logger(__name__)

__snake_case				:      Tuple   		=			OrderedDict(
    [
        ("""audio-spectrogram-transformer""", """ASTFeatureExtractor"""),
        ("""beit""", """BeitFeatureExtractor"""),
        ("""chinese_clip""", """ChineseCLIPFeatureExtractor"""),
        ("""clap""", """ClapFeatureExtractor"""),
        ("""clip""", """CLIPFeatureExtractor"""),
        ("""clipseg""", """ViTFeatureExtractor"""),
        ("""conditional_detr""", """ConditionalDetrFeatureExtractor"""),
        ("""convnext""", """ConvNextFeatureExtractor"""),
        ("""cvt""", """ConvNextFeatureExtractor"""),
        ("""data2vec-audio""", """Wav2Vec2FeatureExtractor"""),
        ("""data2vec-vision""", """BeitFeatureExtractor"""),
        ("""deformable_detr""", """DeformableDetrFeatureExtractor"""),
        ("""deit""", """DeiTFeatureExtractor"""),
        ("""detr""", """DetrFeatureExtractor"""),
        ("""dinat""", """ViTFeatureExtractor"""),
        ("""donut-swin""", """DonutFeatureExtractor"""),
        ("""dpt""", """DPTFeatureExtractor"""),
        ("""encodec""", """EncodecFeatureExtractor"""),
        ("""flava""", """FlavaFeatureExtractor"""),
        ("""glpn""", """GLPNFeatureExtractor"""),
        ("""groupvit""", """CLIPFeatureExtractor"""),
        ("""hubert""", """Wav2Vec2FeatureExtractor"""),
        ("""imagegpt""", """ImageGPTFeatureExtractor"""),
        ("""layoutlmv2""", """LayoutLMv2FeatureExtractor"""),
        ("""layoutlmv3""", """LayoutLMv3FeatureExtractor"""),
        ("""levit""", """LevitFeatureExtractor"""),
        ("""maskformer""", """MaskFormerFeatureExtractor"""),
        ("""mctct""", """MCTCTFeatureExtractor"""),
        ("""mobilenet_v1""", """MobileNetV1FeatureExtractor"""),
        ("""mobilenet_v2""", """MobileNetV2FeatureExtractor"""),
        ("""mobilevit""", """MobileViTFeatureExtractor"""),
        ("""nat""", """ViTFeatureExtractor"""),
        ("""owlvit""", """OwlViTFeatureExtractor"""),
        ("""perceiver""", """PerceiverFeatureExtractor"""),
        ("""poolformer""", """PoolFormerFeatureExtractor"""),
        ("""regnet""", """ConvNextFeatureExtractor"""),
        ("""resnet""", """ConvNextFeatureExtractor"""),
        ("""segformer""", """SegformerFeatureExtractor"""),
        ("""sew""", """Wav2Vec2FeatureExtractor"""),
        ("""sew-d""", """Wav2Vec2FeatureExtractor"""),
        ("""speech_to_text""", """Speech2TextFeatureExtractor"""),
        ("""speecht5""", """SpeechT5FeatureExtractor"""),
        ("""swiftformer""", """ViTFeatureExtractor"""),
        ("""swin""", """ViTFeatureExtractor"""),
        ("""swinv2""", """ViTFeatureExtractor"""),
        ("""table-transformer""", """DetrFeatureExtractor"""),
        ("""timesformer""", """VideoMAEFeatureExtractor"""),
        ("""tvlt""", """TvltFeatureExtractor"""),
        ("""unispeech""", """Wav2Vec2FeatureExtractor"""),
        ("""unispeech-sat""", """Wav2Vec2FeatureExtractor"""),
        ("""van""", """ConvNextFeatureExtractor"""),
        ("""videomae""", """VideoMAEFeatureExtractor"""),
        ("""vilt""", """ViltFeatureExtractor"""),
        ("""vit""", """ViTFeatureExtractor"""),
        ("""vit_mae""", """ViTFeatureExtractor"""),
        ("""vit_msn""", """ViTFeatureExtractor"""),
        ("""wav2vec2""", """Wav2Vec2FeatureExtractor"""),
        ("""wav2vec2-conformer""", """Wav2Vec2FeatureExtractor"""),
        ("""wavlm""", """Wav2Vec2FeatureExtractor"""),
        ("""whisper""", """WhisperFeatureExtractor"""),
        ("""xclip""", """CLIPFeatureExtractor"""),
        ("""yolos""", """YolosFeatureExtractor"""),
    ]
)

__snake_case				:      List[Any]   		=			_LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
						if class_name in extractors:
									a_						:						Tuple 							= model_type_to_module_name(a__)

									a_						:						Any 							= importlib.import_module(f'''.{module_name}'''  ,	"""transformers.models""")
									try:
												return getattr(a__  ,	a__)
									except AttributeError:
												continue

			for _, extractor in FEATURE_EXTRACTOR_MAPPING._extra_content.items():
						if getattr(a__  ,	"""__name__"""  ,	a__) == class_name:
									return extractor

    # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
    # init and we return the proper dummy to get an appropriate error message.
			a_						:						Tuple 							= importlib.import_module("""transformers""")
			if hasattr(a__  ,	a__):
						return getattr(a__  ,	a__)

			return None


def 					_UpperCAmelCase				(						a__  ,	a__ = None  ,	a__ = False  ,	a__ = False  ,	a__ = None  ,	a__ = None  ,	a__ = None  ,	a__ = False  ,	**a__  ,	):

			'''simple docstring'''



			a_						:						List[str] 							= get_file_from_repo(
			    a__  ,	a__  ,	cache_dir=a__  ,	force_download=a__  ,	resume_download=a__  ,	proxies=a__  ,	use_auth_token=a__  ,	revision=a__  ,	local_files_only=a__  ,	)
			if resolved_config_file is None:
						logger.info(
						    """Could not locate the feature extractor configuration file, will try to use the model config instead.""")
						return {}

			with open(a__  ,	encoding="""utf-8""") as reader:
						return json.load(a__)



class        A__:
						"""simple docstring"""






						def __init__(    self    )     -> List[str]:
									raise EnvironmentError(
									    """AutoFeatureExtractor is designed to be instantiated """
									    """using the `AutoFeatureExtractor.from_pretrained(pretrained_model_name_or_path)` method."""    )





						@classmethod
						@replace_list_option_in_docstrings(_lowercase    )
						def 			UpperCamelCase__					(    cls  ,      _lowercase  ,      **_lowercase    )     -> Dict:
									a_						:						Tuple 							= kwargs.pop("""config"""  ,      _lowercase    )
									a_						:						Dict 							= kwargs.pop("""trust_remote_code"""  ,      _lowercase    )
									a_						:						Dict 							= True

									a_							,					a_						:						List[str] 							= FeatureExtractionMixin.get_feature_extractor_dict(_lowercase  ,      **_lowercase    )
									a_						:						Tuple 							= config_dict.get("""feature_extractor_type"""  ,      _lowercase    )
									a_						:						List[Any] 							= None
									if "AutoFeatureExtractor" in config_dict.get("""auto_map"""  ,      {}    ):
												a_						:						Any 							= config_dict["""auto_map"""]["""AutoFeatureExtractor"""]

									# If we don't find the feature extractor class in the feature extractor config, let's try the model config.
									if feature_extractor_class is None and feature_extractor_auto_map is None:
												if not isinstance(_lowercase  ,      _lowercase    ):
															a_						:						List[str] 							= AutoConfig.from_pretrained(_lowercase  ,      **_lowercase    )
												# It could be in `config.feature_extractor_type``
												a_						:						List[Any] 							= getattr(_lowercase  ,      """feature_extractor_type"""  ,      _lowercase    )
												if hasattr(_lowercase  ,      """auto_map"""    ) and "AutoFeatureExtractor" in config.auto_map:
															a_						:						List[str] 							= config.auto_map["""AutoFeatureExtractor"""]

									if feature_extractor_class is not None:
												a_						:						int 							= feature_extractor_class_from_name(_lowercase    )

									a_						:						Dict 							= feature_extractor_auto_map is not None
									a_						:						Union[str, Any] 							= feature_extractor_class is not None or type(_lowercase    ) in FEATURE_EXTRACTOR_MAPPING
									a_						:						Optional[Any] 							= resolve_trust_remote_code(
									    _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase    )

									if has_remote_code and trust_remote_code:
												a_						:						int 							= get_class_from_dynamic_module(
												    _lowercase  ,      _lowercase  ,      **_lowercase    )
												a_						:						Tuple 							= kwargs.pop("""code_revision"""  ,      _lowercase    )
												if os.path.isdir(_lowercase    ):
															feature_extractor_class.register_for_auto_class()
												return feature_extractor_class.from_dict(_lowercase  ,      **_lowercase    )
									elif feature_extractor_class is not None:
												return feature_extractor_class.from_dict(_lowercase  ,      **_lowercase    )
									# Last try: we use the FEATURE_EXTRACTOR_MAPPING.
									elif type(_lowercase    ) in FEATURE_EXTRACTOR_MAPPING:
												a_						:						List[str] 							= FEATURE_EXTRACTOR_MAPPING[type(_lowercase    )]
												return feature_extractor_class.from_dict(_lowercase  ,      **_lowercase    )

									raise ValueError(
									    F'''Unrecognized feature extractor in {pretrained_model_name_or_path}. Should have a '''
									    F'''`feature_extractor_type` key in its {FEATURE_EXTRACTOR_NAME} of {CONFIG_NAME}, or one of the following '''
									    F'''`model_type` keys in its {CONFIG_NAME}: {", ".join(c for c in FEATURE_EXTRACTOR_MAPPING_NAMES.keys()    )}'''    )





						@staticmethod
						def 			UpperCamelCase__					(    _lowercase  ,      _lowercase    )     -> List[Any]:
									FEATURE_EXTRACTOR_MAPPING.register(_lowercase  ,      _lowercase    )


